Ventilator Monitoring, and Sharing the Data with Patients

Martin J. Tobin

Loyola University Medical Center and Hines VA Hospital, Maywood, Illinois

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The primary goal of mechanical ventilation in critically ill patients is to decrease respiratory effort. But success in achieving this goal is variable and often disappointing (1, 2). And although patient effort can be quantified with remarkable accuracy, it is almost never measured in everyday practice. Instead, the ventilator is set by rote, and effort assessed by art rather than science.

The introduction of proportional assist ventilation (PAV) was welcomed as a significant advance because it varies assistance on each breath in proportion to patient demand (3). In contrast, preexisting modes provide a fixed level of assistance irrespective of effort. How does PAV know patient demand? The pressure applied to the respiratory system of a ventilated patient is the sum of pressure from the ventilator and pressure developed by the patient's respiratory muscles (Pmus in physiologists' argot). And these pressures are dissipated in overcoming the resistive and elastic properties of the respiratory system (assuming inertia to be negligible). Since resistive properties are a function of flow and elastic properties a function of volume, the Equation of Motion for the respiratory system can be simplified as:

Airway Pressure + Pmus = (Flow × Resistance)

+ (Volume × Elastance)

Resistance and elastance of the respiratory system are measured on a one-time basis during passive ventilation. Then, by continuously recording airway pressure, flow and volume, it is possible to estimate patient demand, as Pmus, on every breath (3).

Despite compelling logic, PAV has not lived up to its original promise (4). Why? The use of Pmus as a measure of patient effort is based on several assumptionsmany of which are dubious in critically ill patients. In particular, resistance and elastance are unlikely to remain constant over time. A new method for monitoring resistance during PAV is presented by Younes and colleagues in this issue of AJRCCM (pp. 829-839) (5). With this new measurement, termed pulse resistance, it becomes possible to detect increases in resistance over time and provide proportionally more assistance to patientsand vice versa.

Do we need a new means of measuring resistance in ventilated patients? And is it possible to develop a resistance measurement that is really new? Yes, and yes and no. To accurately measure resistance in ventilated patients, the patient's respiratory muscles must be completely silent. This condition is feasible in a research setting, but the required assiduity is less realistic in clinical practice. Because pulse resistance does not require silent muscles, it represents an important advance. But being derived from the Equation of Motion, the bedrock of respiratory mechanics, pulse resistance is not really new although the manipulations of the equation and the technical means of making the measurement are new.

Younes and colleagues produced a transient (0.2 second) fall in flow during early inflation (5). They measured airway pressure, flow, and volume at three points: shortly after ventilator triggering, just before the fall in flow, and at the trough of the negative pulse (5). The investigators reason that the volume measurements have signs in opposite direction and cancel out, making knowledge of elastance unnecessary. Reasoning that Pmus changes at a constant rate over the measurement interval, they eliminate this term. This makes it possible to estimate resistance from recordings of airway pressure and flow (see equation above).

The investigators compared pulse resistance with conventional measurements of resistance during passive ventilation in 67 critically ill patients (5). Agreement was good, with an average difference of about 5%. Pulse resistance assesses airway caliber primarily, in that it does not capture viscoelastic behavior (see Table E1 in the online data supplement to the article).

Is it possible to measure pulse resistance with other ventilator modes? Probably not. With PAV, mechanical inflation is designed to last as long as patient effort (3). But concurrence is less likely with assist-control and pressure support (1, 6). And when an inspiratory effort ends during a negative pulse, large computational errors arise (5).

Leaving PAV aside, this month's report has implications for every critical care physician. It provides the first documentation of how much and how often resistance changes in ventilated patients (see Figure 4 of the report). But how can we tell whether an observed increase in resistance reflects a true decrease in airway caliber and it is not simply a spurious value? Younes and colleagues applied several levels of data smoothing and found that a 5-point moving average provided acceptably narrow confidence intervals (see Figure E7 in the online data supplement). They conclude that a change in resistance of 2 cm H₂O/L/s represents meaningful airway narrowing, and found such a change in two-thirds of patients monitored over about 90 min.

What is the clinical significance of a gradual increase in resistance in a ventilated patient? A build-up of secretions is a possibility; Younes and colleagues noted increases in resistance long before secretions became clinically evident. Their observation complements an earlier report that physical examination has a greater than 40% false-positive and false-negative rate in detecting secretions (7). Although easy to do, suctioning is not without risk: it can cause mucosal damage, severe hypoxemia, and cardiac arrest (8). Conversely, insufficient suctioning will promote atelectasis and pneumonia. The development of a saw-tooth pattern on flow tracings is another strong predictor for secretions, being 6-8 times more likely in patients with secretions than in those without (7).

Basing management decisions on resistance measurements might improve clinical outcome. Repeated measurements of resistance should make it possible to adjust PAV automatically and bring us closer to a closed-loop ventilator, matching patient demand over time (9). Resistance monitoring might also result in safer and more effective suctioning. Would outcome be improved if patients were suctioned only when they developed an increase in resistance of 2 cm H₂O/L/s and also displayed a saw-tooth pattern on their flow tracings (5, 7)? Is this clinical rule far-fetched? Perhaps. But until we incorporate monitored data into algorithms for decision-making and test the algorithms in clinical trials, monitoring will remain a bells-and-whistles affair and patients will not profit from its full potential (10). Short of that step, a deep understanding of respiratory mechanics remains intellectually satisfying for physiciansbut cannot be shared with patients.

	References

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1. Jubran A, Van de Graaff WB, Tobin MJ. Variability of patient-ventilator interaction with pressure-support ventilation in patients with COPD. Am J Respir Crit Care Med 1995; 152: 129-136 [Abstract].

2. Leung P, Jubran A, Tobin MJ. Comparison of assisted ventilator modes on triggering, patients effort, and dyspnea. Am J Respir Crit Care Med 1997; 155: 1940-1948 [Abstract].

3. Younes M. Proportional assist ventilation, a new approach to ventilatory support theory. Am Rev Respir Dis 1992; 145: 114-120 [Medline].

4. Grasso S, Puntillo F, Mascia L, Ancona G, Fiore T, Bruno F, Slutsky AS, Ranieri VM. Compensation for increase in respiratory workload during mechanical ventilation: pressure-support versus proportional-assist ventilation. Am J Respir Crit Care Med 2000; 161: 819-826 [Abstract/Free Full Text].

5. Younes M, Kun J, Masiowski B, Webster K, Roberts D. A method for non-invasive determination of inspiratory resistance during proportional assist ventilation. Am J Respir Crit Care Med 2001; 103: 829-839 .

6. Parthasarathy S, Jubran A, Tobin MJ. Assessment of neural inspiratory time in ventilator-supported patients. Am J Respir Crit Care Med 2000; 162: 546-552 [Abstract/Free Full Text].

7. Jubran A, Tobin MJ. Use of flow-volume curves in detecting secretions in ventilator-dependent patients. Am J Respir Crit Care Med 1994; 150: 766-769 [Abstract].

8. Shim C, Fine N, Fernandez R, Williams MH Jr.. Cardiac arrhythmias resulting from tracheal suctioning. Ann Intern Med 1969; 71: 1149-1153 .

9. Dojat M, Harf A, Touchard D, Lemaire F, Brochard L. Clinical evaluation of a computer-controlled pressure support mode. Am J Respir Crit Care Med 2000; 161: 1161-1166 [Abstract/Free Full Text].

10. Epstein SK, Pauker SG. Principles of decision making. In: Tobin MJ, editor. Principles and practice of intensive care monitoring. New York: McGraw-Hill, Inc.; 1998. p. 149-172.